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Review and prospect of integrated demand response in the multi-energy system

Author

Listed:
  • Wang, Jianxiao
  • Zhong, Haiwang
  • Ma, Ziming
  • Xia, Qing
  • Kang, Chongqing

Abstract

Demand response (DR) is a critical and effective measure to stimulate the demand side resources to interact with renewable generation in the power system. However, the conventional scope of DR cannot fully exploit the interaction capabilities of demand side resources, which limits the energy users in the electric power system. With the revolution of the traditional economic and social pattern based on centralized fossil energy consumption, “Energy Internet” is impelling the development of the third industrial revolution, which aims at promoting the incorporation of sustainable energy and internet technology, and facilitating the integration of multi-energy systems (MESs). By integrating electricity, thermal energy, natural gas and other forms of energy, the smart energy hub (SEH) makes it possible for energy users to flexibly switch the source of consumed energy. With the complementarity of MESs, even the inelastic loads can actively participate in DR programs, which fully exploits the interaction capability of DR resources while maintaining the consumers’ comfort. This novel vision of the DR programs is termed as “Integrated Demand Response (IDR)”. In this context, the state-of-the-art of IDR in the MESs is reviewed for the first time. Firstly, the basic concept of IDR and the value analysis are introduced. The research on IDR in the MES is then summarized. The overviews of the engineering projects around the world are introduced. Finally, the key issues and potential research topics on IDR in the MES are proposed. Hopefully, this paper will provide reference for future research and engineering projects on IDR programs in the MES.

Suggested Citation

  • Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:772-782
    DOI: 10.1016/j.apenergy.2017.05.150
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    References listed on IDEAS

    as
    1. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    2. Evins, Ralph & Orehounig, Kristina & Dorer, Viktor & Carmeliet, Jan, 2014. "New formulations of the ‘energy hub’ model to address operational constraints," Energy, Elsevier, vol. 73(C), pages 387-398.
    3. Orehounig, Kristina & Evins, Ralph & Dorer, Viktor, 2015. "Integration of decentralized energy systems in neighbourhoods using the energy hub approach," Applied Energy, Elsevier, vol. 154(C), pages 277-289.
    4. Wang, Qi & Zhang, Chunyu & Ding, Yi & Xydis, George & Wang, Jianhui & Østergaard, Jacob, 2015. "Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response," Applied Energy, Elsevier, vol. 138(C), pages 695-706.
    5. Qadrdan, Meysam & Chaudry, Modassar & Wu, Jianzhong & Jenkins, Nick & Ekanayake, Janaka, 2010. "Impact of a large penetration of wind generation on the GB gas network," Energy Policy, Elsevier, vol. 38(10), pages 5684-5695, October.
    6. van Ommeren, Bernard J.F. & Allers, Maarten A. & Vellekoop, Michel H., 2017. "Choosing the optimal moment to arrange a loan," Research Report 17007-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    7. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    8. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    9. Yang, Hongming & Xiong, Tonglin & Qiu, Jing & Qiu, Duo & Dong, Zhao Yang, 2016. "Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response," Applied Energy, Elsevier, vol. 167(C), pages 353-365.
    10. Alipour, Manijeh & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2014. "Short-term scheduling of combined heat and power generation units in the presence of demand response programs," Energy, Elsevier, vol. 71(C), pages 289-301.
    11. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    12. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    13. Rong, Aiying & Hakonen, Henri & Lahdelma, Risto, 2006. "An efficient linear model and optimisation algorithm for multi-site combined heat and power production," European Journal of Operational Research, Elsevier, vol. 168(2), pages 612-632, January.
    14. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Robust day-ahead scheduling of smart distribution networks considering demand response programs," Applied Energy, Elsevier, vol. 178(C), pages 929-942.
    15. Bracco, Stefano & Dentici, Gabriele & Siri, Silvia, 2013. "Economic and environmental optimization model for the design and the operation of a combined heat and power distributed generation system in an urban area," Energy, Elsevier, vol. 55(C), pages 1014-1024.
    16. Waheed, M.A. & Oni, A.O. & Adejuyigbe, S.B. & Adewumi, B.A. & Fadare, D.A., 2014. "Performance enhancement of vapor recompression heat pump," Applied Energy, Elsevier, vol. 114(C), pages 69-79.
    17. Sheikhi, Aras & Bahrami, Shahab & Ranjbar, Ali Mohammad, 2015. "An autonomous demand response program for electricity and natural gas networks in smart energy hubs," Energy, Elsevier, vol. 89(C), pages 490-499.
    18. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    19. Hawkes, A.D. & Leach, M.A., 2007. "Cost-effective operating strategy for residential micro-combined heat and power," Energy, Elsevier, vol. 32(5), pages 711-723.
    20. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
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